Another line of research focuses on reconstructing objects and scenes as 3D triangle meshes, which in turn can be used to enable more informed robotic behavior [1]}, [2]}. Reconstructing objects from incomplete scans is challenging. Various approaches exist to reconstruct the geometry of objects, while considering the many facets of the problem [3]}, [4]}, [5]}, [6]}, [7]}, [8]}.
Very recently, Henderson and Ferrari [9]} introduced a network architecture to generate 3D meshes, while only providing single image supervision. At a higher level, our method is related to recent work on deep 3D representation learning for robot-object interaction [10]}, [11]}, [12]}. Moreover, to facilitate the learning of 3D shape representations and the grasping of objects, recent efforts concentrate on curating large shape and grasp repositories [13]}, [14]}, [15]}.
